Glm calibration
Use this skill to learn how to calibrate the General Lake Model (GLM) by adjusting specific parameters like Kw, coef_mix_hyp, wind_factor, lw_factor, and ch to match observed water temperatures and improve overall, annual deep, and summer deep RMSE metrics.From its SKILL.md
npx -y skills add cxcscmu/SkillLearnBench --skill glm-calibrationAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
SKILL.md
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GLM Calibration
Objective
Calibrate the General Lake Model (GLM) by modifying allowed parameters to achieve target RMSE metrics for simulated vs. observed water temperatures.
Allowed Parameters and Ranges
Modify only these parameters in glm3.nml to keep within the published ranges:
Kw(light attenuation):[0.1, 0.5]coef_mix_hyp(hypolimnetic mixing efficiency):[0.3, 0.7]wind_factor(wind speed scaling factor):[0.7, 1.3]lw_factor(longwave radiation scaling factor):[0.7, 1.3]ch(bulk aerodynamic coefficient for sensible heat):[0.0005, 0.002]
Process
- Parse the simulation output from
output/output.nc. - Match simulated temperatures to field observations based on exact datetime and rounded depth. Do not use nearest-time matching, interpolation, or alternative depth binning.
- Compute metrics:
overall_rmse: RMSE across all matched pairs.annual_deep_rmse: RMSE for all matched pairs at rounded depths >= 13 m.summer_deep_rmse: RMSE for months June-September and rounded depths >= 13 m.
- Iteratively adjust parameters to reduce RMSE values until thresholds are met.
What ships with it
Read from the repository
Just SKILL.md. No reference files, no scripts.